LIBOL : a library for online learning algorithms

LIBOL is an open-source library for large-scale online learning, which consists of a large family of efficient and scalable state-of-the-art online learning algorithms for large- scale online classification tasks. We have offered easy-to-use command-line tools and examples for users and developers,...

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Main Authors: Wang, Jialei, Zhao, Peilin, Hoi, Steven C. H.
Other Authors: School of Computer Engineering
Format: Article
Language:English
Published: 2014
Subjects:
Online Access:https://hdl.handle.net/10356/105801
http://hdl.handle.net/10220/20901
http://jmlr.org/papers/volume15/hoi14a/hoi14a.pdf
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Institution: Nanyang Technological University
Language: English
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spelling sg-ntu-dr.10356-1058012020-05-28T07:18:05Z LIBOL : a library for online learning algorithms Wang, Jialei Zhao, Peilin Hoi, Steven C. H. School of Computer Engineering DRNTU::Engineering::Computer science and engineering LIBOL is an open-source library for large-scale online learning, which consists of a large family of efficient and scalable state-of-the-art online learning algorithms for large- scale online classification tasks. We have offered easy-to-use command-line tools and examples for users and developers, and also have made comprehensive documents available for both beginners and advanced users. LIBOL is not only a machine learning toolbox, but also a comprehensive experimental platform for conducting online learning research. Published version 2014-09-17T09:03:51Z 2019-12-06T21:58:11Z 2014-09-17T09:03:51Z 2019-12-06T21:58:11Z 2014 2014 Journal Article Hoi, Steven C. H., Wang, J., & Zhao, P. (2014). LIBOL : a library for online learning algorithms. Journal of machine learning research, 15, 495-499. https://hdl.handle.net/10356/105801 http://hdl.handle.net/10220/20901 http://jmlr.org/papers/volume15/hoi14a/hoi14a.pdf en Journal of machine learning research © The Authors(Journal of Machine Learning Research). This paper was published in Journal of Machine Learning Research and is made available as an electronic reprint (preprint) with permission of The Authors(Journal of Machine Learning Research). The paper can be found at the following official URL: [http://jmlr.org/papers/volume15/hoi14a/hoi14a.pdf]. One print or electronic copy may be made for personal use only. Systematic or multiple reproduction, distribution to multiple locations via electronic or other means, duplication of any material in this paper for a fee or for commercial purposes, or modification of the content of the paper is prohibited and is subject to penalties under law. 6 p. application/pdf
institution Nanyang Technological University
building NTU Library
country Singapore
collection DR-NTU
language English
topic DRNTU::Engineering::Computer science and engineering
spellingShingle DRNTU::Engineering::Computer science and engineering
Wang, Jialei
Zhao, Peilin
Hoi, Steven C. H.
LIBOL : a library for online learning algorithms
description LIBOL is an open-source library for large-scale online learning, which consists of a large family of efficient and scalable state-of-the-art online learning algorithms for large- scale online classification tasks. We have offered easy-to-use command-line tools and examples for users and developers, and also have made comprehensive documents available for both beginners and advanced users. LIBOL is not only a machine learning toolbox, but also a comprehensive experimental platform for conducting online learning research.
author2 School of Computer Engineering
author_facet School of Computer Engineering
Wang, Jialei
Zhao, Peilin
Hoi, Steven C. H.
format Article
author Wang, Jialei
Zhao, Peilin
Hoi, Steven C. H.
author_sort Wang, Jialei
title LIBOL : a library for online learning algorithms
title_short LIBOL : a library for online learning algorithms
title_full LIBOL : a library for online learning algorithms
title_fullStr LIBOL : a library for online learning algorithms
title_full_unstemmed LIBOL : a library for online learning algorithms
title_sort libol : a library for online learning algorithms
publishDate 2014
url https://hdl.handle.net/10356/105801
http://hdl.handle.net/10220/20901
http://jmlr.org/papers/volume15/hoi14a/hoi14a.pdf
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